With the popularity of unmanned vehicles including unmanned aerial vehicles (UAVs) and environment surveillance applications, airborne networks (ANs) have become important platforms for wireless transmissions in the sky.
Today there are numerous unmanned vehicles (UVs) including UAVs (unmanned aerial vehicles), UGVs (unmanned ground vehicles), UUWVs (unmanned underwater vehicles) and products for either civilian or military applications, and the use of UVs is expected to grow dramatically in the future. For example, Amazon plans to use UAVs to deliver customers' goods; Tesla and others have designed and continue to develop UGVs; vehicles; UAVs are used for environment surveillance/weather reporting; and there are numerous other applications and more are being developed.
With the increase of UVs, collaboration communications have become important because many nodes can cooperate to wirelessly relay network data for a long distance. For example, remote sensing can be performed via multi-hop UAV communications. However, UV communications also bring many challenges. First, it is desired to see them smartly cooperate with each other to avoid wireless transmission conflicts. They should send out data without much traffic congestion. Second, it is desired that the network has high-speed data delivery. With the coming big data era, many nodes need to schedule their communications carefully to deliver as much data as possible.
In some instances, UVs use hierarchical hybrid wireless network (H2WN) comprised of a two-level topology and hybrid antenna systems (omni-/multi-directional antennas). In the high-level network, it has a relatively sparse topology comprising powerful nodes that can communicate with each other with long-distance links. Those nodes are equipped with multi-directional antennas in order to achieve a multi-beam high-rate transmission. In the low-level network, it has a much higher node density than the high-level network. Those nodes also have much lower mobility. To reduce the cost, they are equipped with omni-directional antennas.
Such a hierarchical network can be seen in many applications such as airborne networks, smart factory, sensor and actuator networks, and the like. The reason that such a hierarchical architecture is popular is because of its separation of different types of wireless nodes, easy network management, and good balance between cost and throughput. For instance, the expensive high-level nodes can be used to deliver the high-throughput data that is generated from many low-level cheap nodes. Typically, there is a ‘sink’ node in the low-level network that can collect event data from any node. The high-level network often has a node that serves as the role of ‘commander’ that can control the whole network.
Today's antenna systems have been improved drastically these years due to the rapid development of circuits and materials. The inexpensive popular antenna is omni-directional antenna. It simply radiates the same signal to all directions even though it may target only a specific node. Thus, it can cause interference among the neighbors. To better focus the energy on a specific direction, directional antenna with one beam (also called unidirectional antenna) can be used. Since all signal energy is concentrated in a single angle, it avoids the interference with other directions, and also is able to send data for a longer distance than omnidirectional antenna. A multiple-input and multiple-output (MIMO) antenna has complex antenna matrix coefficient control and needs the receiver's real-time feedback. Another antenna is the multi-beam smart antenna (MBSA). The MBSA extends the single-beam antenna to multi-beam structure, and allows independent transceivers to be used in each beam. Each beam can send out different data according to its own queue management policy. Thus, a MBSA significantly improves the throughput through simultaneous data transmissions in multiple directions. Since it does not rely on the receiver's feedback, some beam directions may have poor performance. But those beams can ‘help’ each other by using detour beam transmissions.
There are challenges in the use of H2WN networks for UVs. In particular, there is a need to address routing protocol design issues.
For low-level routing, there is a desire to improve general event-to-sink routing search in H2WN networks for UVs. In particular, the sink's singular mobility needs to be addressed for UV applications. Unlike the high-level nodes that have ‘even mobility’, that is, each node has similar mobility (for aircraft network, it is generally 80-120 m/s), the low-level network has ‘singular mobility’, that is, most nodes may have little or low mobility, while the sink could have high mobility due to its task of global data collection. To handle the sink's singular mobility, even an on-demand (reactive) routing could not fast track the sink's sudden leaving, not mentioning the proactive routing that only works well for semi-static network topology. As a matter of fact, since most of other low-level nodes do not have such sudden leaving, the global path search via blind RREQ broadcasting would cause much routing overhead in a high-density, low-rate ad hoc network.
For high-level routing, one challenge is how to maximize the benefits of multi-beam antennas in the routing process. Conventional ad hoc routing schemes such as DSR only use one path to deliver the data. Thus, they only need one of the beams in the MBSAs for communications. General multi-path routing schemes often use widely disjointed paths to deliver data. Such disjoint paths do not have many intersection points among them. Since only those intersection points can possibly use multiple beams to deliver data, the nodes cannot fully explore all the available beams to improve the throughput. It is challenging to design a low-overhead, high-throughput, multi-beam routing among the long-distance links.
Regarding cross-level routing, an important issue is how an event node in the low level can quickly localize the commander node in the high level, and then efficiently forwards the data to a low-level node that is closest to the commander node. Note that the high-level network is much sparser than the low-level network. Thus it is difficult for an event node to find the closest high-level node.
Therefore, there exists a need to overcome challenges in the art, some of which are described above.